Driven by the prevailing atmosphere of the sport and the demands of the users, a large number of photographs or videos are recorded in each of the sports event (Marathons, races, games, . . . etc.). More and more sponsors or sports websites provide services for athletes to search for photos or videos (films) using the characters (for example, numbers, etc.) marked on a number cloth (also referred to as “bib number”) so that participants could search for their own photos or videos after the racing event and share them to a social network. Generally, such recognition is performed through a computer for image analyzing and processing. However, as the displacement and deformation of the number cloth(s) in a photograph or video makes the recognition rate of characters on a number cloth low by recognition using the computer, it is now mostly performed by hiring a large number of laborers for manual recognition to mark the characters on the number cloth(s) on a photograph or a video, thereby spending a lot of manpower, time and cost.
Recently, with the improvement of the neural network technology, the recognition effect is greatly improved compared with the previous technologies, and the neural network may be widely used, for example, for text recognition. Conventionally, the text recognition of neural networks may include the text detection, text segmentation and character recognition etc. steps. Although the use of neural networks may enhance the recognition effect, the characters on a number cloth is susceptible to the distortions of the number cloth and thus may not be completely segment during the recognition, leading to the subsequent optical character recognition (OCR) prone to errors and affecting the accuracy of the text recognition.